{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7H2TAZDSJ5J2MVRVDOHVT6XN72","short_pith_number":"pith:7H2TAZDS","schema_version":"1.0","canonical_sha256":"f9f53064724f53a656351b8f59faedfeb41cd0746f411fa7cebd8533f77af876","source":{"kind":"arxiv","id":"2605.24548","version":1},"attestation_state":"computed","paper":{"title":"Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.PR"],"primary_cat":"cs.LG","authors_text":"Hao Wang, Thibaut Mastrolia, Yan Leng","submitted_at":"2026-05-23T12:32:50Z","abstract_excerpt":"Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observed history alone. Classical jump-diffusion models offer a principled mathematical framework but assume rigid parametric forms, while recent neural jump models operate on fully observed trajectories without inferring the hidden states that govern the dynamics. We propose \\textit{Deep ZakaiJ}, a latent-state model for partially observed jump-diffusion systems that embeds the Zakai nonlinear filtering equation into a neural encoder--decoder archit"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.24548","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T12:32:50Z","cross_cats_sorted":["math.PR"],"title_canon_sha256":"ab051683b1bb69919feb6cd69c693035087c751d17f1a9980c926e392ccc6817","abstract_canon_sha256":"f5626e18d2c517687faa4db840d23e0810c853cc498d95349267abe410e491b4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:45.818941Z","signature_b64":"MOJHpnTDBwEkL0Hpgzi6XDyj/TSzJhzq/MWxzIYELKK+Mxm9tsyk7VlnekhH+8v43GRArSLEjzN8VPMvj+avDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9f53064724f53a656351b8f59faedfeb41cd0746f411fa7cebd8533f77af876","last_reissued_at":"2026-05-26T01:03:45.818106Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:45.818106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.PR"],"primary_cat":"cs.LG","authors_text":"Hao Wang, Thibaut Mastrolia, Yan Leng","submitted_at":"2026-05-23T12:32:50Z","abstract_excerpt":"Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observed history alone. Classical jump-diffusion models offer a principled mathematical framework but assume rigid parametric forms, while recent neural jump models operate on fully observed trajectories without inferring the hidden states that govern the dynamics. We propose \\textit{Deep ZakaiJ}, a latent-state model for partially observed jump-diffusion systems that embeds the Zakai nonlinear filtering equation into a neural encoder--decoder archit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24548","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.24548/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.24548","created_at":"2026-05-26T01:03:45.818238+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24548v1","created_at":"2026-05-26T01:03:45.818238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24548","created_at":"2026-05-26T01:03:45.818238+00:00"},{"alias_kind":"pith_short_12","alias_value":"7H2TAZDSJ5J2","created_at":"2026-05-26T01:03:45.818238+00:00"},{"alias_kind":"pith_short_16","alias_value":"7H2TAZDSJ5J2MVRV","created_at":"2026-05-26T01:03:45.818238+00:00"},{"alias_kind":"pith_short_8","alias_value":"7H2TAZDS","created_at":"2026-05-26T01:03:45.818238+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72","json":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72.json","graph_json":"https://pith.science/api/pith-number/7H2TAZDSJ5J2MVRVDOHVT6XN72/graph.json","events_json":"https://pith.science/api/pith-number/7H2TAZDSJ5J2MVRVDOHVT6XN72/events.json","paper":"https://pith.science/paper/7H2TAZDS"},"agent_actions":{"view_html":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72","download_json":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72.json","view_paper":"https://pith.science/paper/7H2TAZDS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24548&json=true","fetch_graph":"https://pith.science/api/pith-number/7H2TAZDSJ5J2MVRVDOHVT6XN72/graph.json","fetch_events":"https://pith.science/api/pith-number/7H2TAZDSJ5J2MVRVDOHVT6XN72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72/action/storage_attestation","attest_author":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72/action/author_attestation","sign_citation":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72/action/citation_signature","submit_replication":"https://pith.science/pith/7H2TAZDSJ5J2MVRVDOHVT6XN72/action/replication_record"}},"created_at":"2026-05-26T01:03:45.818238+00:00","updated_at":"2026-05-26T01:03:45.818238+00:00"}